A System And Method For Computer-Assisted Planning Of A Trajectory For A Surgical Insertion Into A Skull

ABSTRACT

A computer system and corresponding method are used to assist in planning a trajectory for a surgical insertion into a skull to a target representing an anatomical region. The computer system is configured to: provide the computer system with a three-dimensional image representation of the skull and brain which has been parcellated into anatomical regions, including an identification of critical objects comprising structures within the brain to be avoided during the surgical insertion; provide the computer system with a region of interest comprising an anatomical region within the brain representing the target of the trajectory for the surgical insertion; determine a metric for voxel locations within the anatomical region corresponding to the region of interest, the metric representing the suitability of each of the voxel locations to be a target location for the trajectory; select a set of one or more voxel locations having the greatest suitability according to the metric within the region of interest, each of the one or more selected voxel locations representing a potential target location for the trajectory; and identify a trajectory for the surgical insertion to a potential target location in the region of interest.

FIELD OF THE INVENTION

The present invention relates to a system and method forcomputer-assisted planning of a trajectory for a surgical insertion intoa skull (intra-cranial insertion).

BACKGROUND OF THE INVENTION

Approximately one-third of individuals with focal epilepsy continue tohave seizures despite optimal medical management, e.g. withanti-epileptic drugs. These patients are candidates for resection if theepileptogenic zone (EZ) can be identified (and then resected withoutimpacting cortical function). In about 25% of surgical candidates, theEZ cannot be inferred from non-invasive imaging data [10]. Thesepatients may undergo surgical intra-cerebral electrode implantation torecord electoencephalography signals to help identify where seizuresstart and rapidly propagate. Implanted electrodes may also be used forstimulation studies to map eloquent areas, e.g. motor or sensory cortex,and to determine whether a safe resection may be made that removes theEZ without compromising eloquent cortex. However, such invasiveprocedures carry a risk of haemorrhage, neurologic deficit, andinfection.

Electrodes are placed to assess (1) deep anatomical regions of interest(ROIs) (e.g. a hippocampus or insula), identified as a potential EZ, and(2) superficial ROIs, which are nearby functional areas around thepotential EZ (e.g. motor or sensory cortex). The superficial ROIs aretargeted to determine any overlap between the EZ and functional areas toaid in planning safe resection margins. The electrodes must avoidcritical structures (e.g. blood vessels, sulci) to minimise the risk ofcomplications such as haemorrhage.

Preoperative planning of electrode placement is a necessary prerequisiteto implantation. Important anatomical and functional landmarks of thebrain (such as blood vessels, pial boundaries, nerve tracts, etc.) canbe identified with advanced neuro-imaging and image-processingtechniques. Usually multiple electrodes are to be implanted, eachelectrode having a trajectory defined by a brain target and a skullentry. For example, a trajectory may extend from a respective entrypoint on the skull to a respective target point in a deep ROI. Inparticular, the trajectory for each electrode is selected to record froma deep ROI, and potentially one or more superficial ROIs, while alsoavoiding critical structures and interference with other electrodes. Theoverall configuration of electrodes to be implanted is arranged toachieve adequate cortical coverage and pass through safe, avascularplanes.

The clinical gold standard for the preoperative planning of electrodeplacement is manual planning of each electrode trajectory by visualinspection of pre-operative imaging data. The trajectories are placed toattain dep and superficial ROIs, avoid critical structures, sample greymatter (GM), and avoid conflict between electrodes. Maximal sampling ofGM is preferred, as epileptic seizures arise in GM rather than in whitematter. However, such planning is a complex, time-consuming task.Automated trajectory planning algorithms can help to reduce planningtime while improving safety and EEG signal acquisition (e.g. increasedGM sampling) by optimising trajectories using quantitative measures ofelectrode path suitability.

Various single trajectory planning methods have been presented whichrequire the user to specify a target as a point (spatial coordinate) [2,6, 8] or a manually identified ROI. A potential trajectory is discardedif it is surgically infeasible, e.g. it the angle of entry at the entrypoint is too far from the normal, or the overall length of thetrajectory is too long. A potential trajectory is further discarded ifit passes an unsafe distance from any critical structures, such as bloodvessels and sulci. The remaining trajectories (which have not beendiscarded) are scored and sorted according to a risk metric, and themost suitable trajectory is selected. However, these methods havecertain limitations when applied to electrode implantation for epilepsy.For example, for epilepsy, clinicians have a list of target ROIs fromwhich it is desired to obtain EEG signals, and the most suitable targetpoint within each ROI may not be readily apparent. Furthermore, sincemultiple electrodes typically are to be implanted simultaneously, theymust be placed to avoid conflicts between electrodes (while at the sametime trying to maximise the coverage area for recording, e.g. the GMsampling).

Multiple trajectory planning algorithms designed for electrodeimplantation for epilepsy allow a user to identify target and entryregions [4, 7]. Reference [4] determines trajectories by randomlysampling from regions near user selected targets and entry points. Forevery target, the best trajectory in terms of entry angle, avoidance ofcritical structures, and lack of conflict between electrodes iscomputed. Reference [7] determines candidate target points within thehippocampus and amygdala by randomly sampling a Gaussian distributiondefined on the ROI distance map, in which targets far from the surfaceof the ROI are preferentially sampled. Trajectories are removed fromconsideration if they intersect critical angles or have a surgicallyinfeasible entry angle. Finally the best trajectories are determined bymaximising grey matter capture, minimising risk score, and ensuringthere is no mutual interference between trajectories. (Note that eachelectrode may have multiple contacts distributed along its length, eachcapable of recording a signal for that location, so that maximising greymatter sampling is therefore impacted by the placement of theseindividual contacts according to a given trajectory).

WO 2015/173571 (which is hereby incorporated by reference into thepresent application) describes a system and method for thecomputer-assisted planning of trajectories for surgical insertion into askull, including determining a trajectory to a given target location,for example based on surgical feasibility, lack of interference withother electrode trajectories, and a low risk factor. As disclosed in WO2015/173571, a user may specify that an electrode is to be placed withina particular anatomical region. It is suggested that this could beaccomplished by computer sampling the specified region to provide a setof target locations, determining a trajectory to each of these targetlocations, and then selecting the target location within the region forwhich the respective trajectory has the lowest assessed risk. Althoughthis approach provides an appropriate trajectory to a target point inthe region, it is rather expensive in terms of computational resources,especially if electrodes are to be placed in multiple regions.

SUMMARY OF THE INVENTION

The invention is defined in the appended claims.

A method and apparatus are provided to assist in planning a trajectoryfor a surgical insertion into a skull to a target representing ananatomical region. The apparatus is configured to perform the methodcomprising: providing the computer system with a three-dimensional imagerepresentation of the patient anatomy comprising the skull and brainwhich has been parcellated into anatomical regions, including anidentification of critical objects comprising structures within thebrain (i.e. internal to the skull) to be avoided during the surgicalinsertion; providing the computer system with a region of interestcomprising an anatomical region within the brain representing the targetof the trajectory for the surgical insertion; determining a metric forvoxel locations within the anatomical region corresponding to the regionof interest, the metric representing the suitability of each of thevoxel locations to be a target location for the trajectory; selecting aset of one or more (e.g. several) voxel locations having the greatestsuitability according to the metric within the region of interest, eachof the one or more selected voxel locations representing a potentialtarget location for the trajectory; and identifying a trajectory for thesurgical insertion to a potential target location in the region ofinterest.

The methods described here can be used to support planning a surgicalinsertion or incision for a wide range of clinical purposes. Forexample, the surgical incision may be utilised to implant an electrodefor performing (stereo) electroencephalography. In this context, theremay be multiple electrodes to insert, each involving planning arespective surgical incision. Other clinical purposes for planning asurgical insertion may be to insert a stent, to perform deep brainstimulation or a tumour biopsy, the placement of other probes in thebrain: e.g. to drain cysts or ventricles, to introduce a medical laserdevice into a particular part by a safe trajectory, and/or to deliverfocal therapy. (It will be appreciated that this listing of potentialclinical purposes is by way of example only, and is not intended to beexhaustive).

It will be appreciated that the precise nature of the instrument ordevice being inserted will vary according to the particular clinicalcontext, likewise the criteria utilised for assessing the suitability ofany given trajectory. For example, for investigations relating toepilepsy, the criteria may include a cost function based on theproportion of the trajectory associated with an entry point which passesthrough grey matter compared with the proportion of the trajectory whichpasses through white matter. This is because the electrode may have themultiple contacts typically spaced along such an electrode, and theseshould be located in grey matter in order to obtain a desired signal.Having a higher proportion of the trajectory in grey matter increasesthe likelihood that the contacts will be located, as desired, in greymatter. However, this distinction between grey matter and white mattermay be less (or not at all) relevant in other clinical contexts, such asfor performing a tumour biopsy. Similarly, other aspects of the planningprocedure may vary as appropriate according to the clinical context. Forexample, while major blood vessels are always expected to representcritical objects or structures to be avoided, the inclusion of certainother anatomical features as critical objects may depend upon theparticular clinical context.

In some implementations, the computer system provides a graphicalvisualization of a trajectory which helps a user to understand betterany risks associated with the trajectory, in particular, the portion(s)of the trajectory that is/are closest to critical structures. In someimplementations, the graphical visualisation of a trajectory is linkedto at least one two-dimensional or three-dimensional view derived by thecomputer system from the three-dimensional representation of the skulland of critical objects located within the skull.

Also provided is a computer program comprising program instructions inmachine-readable format that when executed by one or more processors ina computer system cause the computer system to implement any of thevarious methods as described above. These program instructions may bestored on a non-transitory computer readable storage medium, such as ahard disk drive, read only memory (ROM) such as flash memory, an opticalstorage disk, and so on. The program instructions may be loaded intorandom access memory (RAM) for execution by the one or more processorsof a computer system from the computer readable storage medium. Thisloading may involve first downloading or transferring the programinstructions over a computer network, such as a local area network (LAN)or the Internet.

Also provided herein is an apparatus comprising a computer systemconfigured to implement the various methods as described above. Thecomputer system may comprise one or more machines, which may be generalpurpose machines containing one or more processors for running programinstructions (as described above), configured to perform such methods.The general purpose machines may be supplemented with graphicsprocessing cards units (GPUs) to provide additional processingcapability, e.g. for parallel computations, such as assessing differenttarget locations or different potential trajectories. The computersystem may also comprise at least some special purpose hardware forperforming some or all of the processing described above, such asdetermining the visualisations. The computer system may be incorporatedinto apparatus specifically customised for performing computer-assistedplanning of a trajectory for a surgical incision. Such apparatus mayalso be used to provide support during the surgical operation itself,such as by providing real-time visualisations of the position of aninserted instrument (potentially in relation to the planned trajectory),and/or by providing visualisations of data acquired during the surgicaloperation.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention will now be described in detail byway of example only with reference to the following drawings:

FIG. 1 is a schematic diagram of an interactive tool for computerassisted planning of intra-cranial surgical insertions in accordancewith some embodiments of the invention.

FIGS. 2A and 2B show examples of implantation plans for intra-cranialsurgical insertions, with FIG. 2A highlighting deep ROIs and FIG. 2Bhighlighting superficial ROIs.

FIGS. 3A, 3B and 3C are axial, sagittal and coronal views respectivelyof the metric determined by the approach described herein for thehippocampus, and a trajectory selected by the approach described herein.

FIGS. 4A-4D are graphs comparing the suitability of trajectoriesdetermined by the approach described herein with trajectories determinedby manual planning.

FIGS. 5A and 5B show for comparison trajectories determined by theapproach described herein and trajectories determined by manualplanning, with FIG. 5A including the cortex and FIG. 5B excluding thecortex.

FIG. 6 is a flowchart showing the approach described herein fordetermining a trajectory for a surgical insertion.

DETAILED DESCRIPTION

Some embodiments of the present invention provide a tool for anatomydriven, multiple trajectory planning (ADMTP), which uses patientspecific anatomy to compute trajectories. The ADMTP tool requires asuser input only the anatomy names for the deep and superficial ROIs ofinterest (as described in more detail below). The ADMTP tool thenselects target points within the specified deep ROIs according tosafety, measured in terms of distance from critical structures. The besttrajectories to these selected target points can then be computed toattain superficial ROIs, avoid critical structures, improve grey mattersampling, and avoid conflicts between electrodes.

A) METHODOLOGY

The ADMTP tool takes as input a patient specific image of the brain,which represents a 3-dimensional spatial map of the brain, correspondingto or derived from one or more images of the brain of the patient whichhave been acquired using any appropriate neuro-imaging technique(s). Thepatient specific image is annotated to provide parcellation and criticalstructure segmentation, in effect specifying anatomical regions withinthe brain and critical structures. For example, for parcellation, eachpixel/voxel within the patient specific image is labelled according tothe specific anatomical region that contains (or is represented by) thatpixel. Critical structures (e.g. arteries, veins and sulci) arespecifically identified and segmented, likewise grey matter is alsoidentified. Note that a critical structure, such as a blood vessel, maypass through various anatomical regions (potentially including one ormore region of interests).

In some implementations, this parcellation and/or segmentation may beperformed at least part in automated fashion by computer. In addition,the parcellation and/or segmentation may be performed at least in partwith respect to the original acquired images, and then transferred tothe final patient specific image(s) of the brain to be used fortrajectory planning. For example, blood vessels (veins and arteries) maybe identified and segmented using computed tomography (CT) angiographyand/or T1-weighted magnetic resonance imaging (MRI) with gadoliniumenhancement using multi-scale, multi-modal tensor voting vesselextraction [9]. The skull can be segmented from CT data withthresholding and morphologic dilation. Geodesic information flow (GIF)[3] may be used to parcellate the brain from T1 weighted MRI data, forexample, using the Desikan-Killiany-Tourville (DKT) protocol [11]. Itwill be appreciated that other implementations may use differentapproaches for performing the parcellation and/or segmentation,including if so desired manual parcellation and/or segmentation (byhand).

A user is now able to select various anatomical regions (as determinedby the parcellation) as deep or superficial ROIs from thepatient-specific image of the brain. In one example implementation, atotal of 208 brain regions are identified by the parcellation, and arethen available for selection as deep or superficial ROIs.

The ADMTP tool is provided with the parcellated imaging data, thesegmented critical structures (objects), and a list of ROIs (deep andsuperficial). For a list of user-defined ROIs, ADMTP then finds animplantation plan V(N)=[v₁, . . . , v_(N)], where v_(n) represents thetrajectory for the n^(th) electrode. For v_(n), M candidate targetpoints T_(n,i): i∈{1, . . . , M} are calculated. ADMTP then determines Pentry points E_(n,j) j∈{1, . . . , P} for each T_(n,i) and finds acombination of T_(n,i) and E_(n,j) for all N electrodes that avoidsinterference between the electrodes.

For a given ROI, target point selection determines M candidate targetpoints T_(n,i): i∈{1, . . . , M} for the n^(th) electrode. A target riskimage is defined as C_(t)=[C, f_(t)(c): c∈C] where C is the grid ofimage voxel locations and f_(t)(c) is the target risk score for thegiven voxel c. f_(t)(c) is computed as:

$\begin{matrix}{{f_{t}(c)} = \left\{ \begin{matrix}{1,} & \; & {{{if}\mspace{14mu} c} \notin \Omega_{roi}} \\{1,} & \; & {{{if}\mspace{14mu} c} \in \Omega_{roi}} \\\; & \begin{matrix}{{\omega_{roi}*\left( {1 - \frac{f_{roi}(c)}{\max \left( f_{roi} \right)}} \right)} +} \\{{\omega_{cri}*\left( {1 - \frac{f_{cri}(c)}{\max \left( f_{cri} \right)}} \right)},}\end{matrix} & {else}\end{matrix} \right.} & \left( {{Equation}\mspace{14mu} 1} \right)\end{matrix}$

In which Ω_(roi) is the set of voxels in the deep ROI, Ω_(cri) is theset of voxels in the critical structures, f_(roi) is the distancebetween voxel c and the closest surface point on the deep ROI,calculated using a bounding volume hierarchy (BVH) as in [8]. Similarly,f_(cri) is the distance between voxel c and the closest surface point onall critical structures. In addition, ω_(roi) and ω_(cri) control therelative importance of placing the target within the deep ROI andavoiding critical structures, respectively. Target points are selectedfrom c∈C_(t) by calculating local minima using the watershed algorithm[1] and then sampling the M lowest values of f_(t)(c) with a distance ofat least d_(tar) between every target point T_(n,i).

In other words, f_(t)(c) has a value of 1 for any voxel that is notwithin the specified ROI, or that is within a critical structure. Suchvoxels will therefore not be selected as a target location for thespecified ROI. The remaining voxels (which are inside the specified ROIbut outside any critical structure) are candidate target locations. Theselection between these candidate target locations is determinedaccording to a metric based on two factors. The first factor representsthe distance from (within) the surface of the ROI, normalised by themaximum distance of any candidate target location from the surface ofthe ROI, the second factor represents the distance from (outside) thenearest surface of a critical structure, normalised by the maximumdistance to the nearest surface of a critical structure.

In general terms, it is good to be a long way from the nearest surfaceof a critical structure (because this enhances safety), i.e. increasingf_(roi)(c); likewise, it is generally good to be a long way from thesurface of the relevant ROI (e.g. because this can provide more reliablesignal recording), i.e. increasing f_(cri)(c). Each term in brackets inEquation 1 therefore ranges from unity (the voxel is located on thesurface of the ROI or a critical structure), down to zero (the voxel islocated at the maximum distance from the surface of the ROI or acritical structure), where a lower value is better. The metric is thenbased on the weighted sum of these first and second brackets(corresponding to the first and second factors), according to relativethe values of ω_(roi) and ω_(cri) (such that a higher value of theweight gives the corresponding factor a relatively larger influence onthe final value of the metric). In the case that ω_(roi) and ω_(cri) arechosen to sum to unity, then the metric ranges in value between zero andunity, with a lower score being better. For example, Table 1 (below)gives example values of 0.25 and 0.75 respectively for that ω_(roi) andω_(cri), indicating that avoiding a critical structure (object) is givena higher weighting than depth inside the ROI.

Note that in Equation 1 above, f_(t)(c) is defined so that a high valueof the metric is good—i.e. more suitable. However, in otherimplementations, f_(t)(c) might be defined so that a low value of themetric is more suitable. References herein to a maximum of the metricshould be understood as indicating a (potentially local) high value ofsuitability.

It will also be appreciated that other approaches can be used forcalculating the target risk score. For example, rather than determiningf_(roi)(c) based on the distance from the given location (c) to anysurface of the ROI (as above), another way to determine f_(roi)(c) isbased on the proximity of the given location (c) to the medial surfaceof the ROI—i.e. the surface that faces the midline (plane of left-rightsymmetry). Having a location (c) close to the medial surface may bebeneficial (more suitable) if it is desired to locate the electrode indeep grey matter.

Note that the calculation of the metric for voxel c is determined onlyfrom the location of c (and the surfaces of the ROI and any nearbycritical structures). In particular, the value of the metric is notdependent upon any putative trajectory to voxel c. This allows themetric to be calculated relatively quickly (since there is no need toperform calculations along the length of a trajectory). Once the metrichas been determined, a number of local maxima within the ROI areidentified. For example, M target locations can be identified bysampling the M lowest values of f_(t)(c), subject to a constraint thatthere is a distance of at least d_(tar) between each target pointT_(n,i). These M target locations can be considered as promisingend-points for a trajectory, because they are relatively deep inside thespecified ROI, and relatively far away from any critical structure.

In another implementation for finding M target locations, iterativeflooding [1] is used to find a set of voxels Q corresponding to localminima of f_(t)(c). The voxels p, where p∈Q, are then spatiallyclustered into K groups using K-means clustering [12], where K is setequal to the number of electrodes to be located in that particular ROI.For the k^(th) electrode, the final target points are the M points inthe k^(th) cluster with the highest values of f_(t)(c).

Given the set of M target locations within the ROI as identified above,a trajectory to each target location within the ROI can be determined asfollows. Entry points, defined as E_(n,j): j∈{1, . . . , P}, arecomputed by using all vertices on the skull mesh; in one implementation,this is roughly 10000 points sampled every 0.2 mm³. Potentialtrajectories T_(n,i)E_(n,j) : i∈{1, . . . , M}, j∈{1, . . . , P}, arethen removed from consideration using a modified approach of [8] asfollows:

1. T_(n,i)E_(n,j) is longer than d_(len).2. The angle between T_(n,i)E_(n,j) and the skull normal is greater thand_(ang).3. T_(n,i)E_(n,j) does not traverse a desired superficial ROI.4. T_(n,i)E_(n,j) intersects a critical structure (arteries, veins, orsulci).If a trajectory falls into any of the above categories, it is excluded.If no suitable trajectories are found, the values of d_(len) and/ord_(ang) may be iteratively relaxed, for example d_(len) may be steppedup by 10 nm (e.g. to a maximum of 110 mm), and/or d_(ang) may be steppedup by 10° (e.g. to a maximum of 45°), until a suitable set oftrajectories is found. For example, after the above filtering based onthese hard criteria, typically there may be 1000-5000 potentialtrajectories per electrode. The remaining trajectories then have a riskscore R_(n,i,j) and a GM ratio G_(n,i,j) calculated. R_(n,i,j) is ameasure of the distance to critical structures and may be computed as:

$\begin{matrix}{R_{n,i,j} = \frac{{\int_{E_{n,j}}^{T_{n,i}}d_{risk}} - {\left( {{f_{cri}(x)} - d_{safe}} \right)\ {dx}}}{\left( {d_{risk} - d_{safe}} \right)*{length}}} & \left( {{Equation}\mspace{14mu} 2} \right)\end{matrix}$

where trajectories with f_(cri)(x) closer than d_(safe) have the highestrisk (R_(n,i,j)=1), while f_(cri)(x) greater than d_(risk) have no risk(R_(n,i,j)=0). N.B. In Equation 2, length represents the length of thetrajectory, i.e. the distance (or integral of xdx) between T_(n,i) andE_(n,j), while other terms used in Equation 2 are defined in Table 1below.

G_(n,i,j) measures the proportion of electrode contacts in GM. For eachelectrode, Q contacts with a recording radius of p_(r) are spaced ateven intervals p_(g) along the trajectory. G_(n,i,j) is calculated as:

$\begin{matrix}{G_{n,i,j} = \frac{\begin{matrix}{{\sum\limits_{q = 1}^{Q}\; {H\left\lbrack {f_{gm}\left( {p_{q} - p_{r}} \right)} \right\rbrack}} + {H\left\lbrack {f_{gm}\left( p_{q} \right)} \right\rbrack} +} \\{H\left\lbrack {f_{gm}\left( {p_{q} + p_{r}} \right)} \right\rbrack}\end{matrix}}{3*Q}} & \left( {{Equation}\mspace{14mu} 3} \right)\end{matrix}$

where f_(gm)( ) is the signed distance from the GM surface and H[ ] isthe Heaviside function, with values of 1 inside GM and 0 outside. Eachtrajectory is assigned a weighted score S_(n,i,j) computed asS_(n,i,j)=10*R_(n,i,j)+G_(n,i,j), where the factor of 10 was determinedempirically to that low risk is prioritized over a high GM ratio.

The final implantation plan V(N) is found by optimizing:

$\begin{matrix}{{S_{total} = {\underset{V{(n)}}{argmin}\left( {\frac{1}{N}{\sum\limits_{n = 1}^{N}S_{n,i,j}}} \right)}}{{subject}\mspace{14mu} {to}}\; {D\left( {\overset{\_}{T_{n,\iota}E_{n,j}},{\overset{\_}{T_{k,\iota}E_{k,j}} > {d_{traj}:{\forall n}}},{\forall{k \in {{\left\{ {1,\ldots \mspace{14mu},N} \right\} n} \neq k}}}} \right.}} & \left( {{Equation}\mspace{14mu} 4} \right)\end{matrix}$

where d_(traj) specifies the minimum distance between trajectories thatdo not conflict. Due to the constraint d_(traj), if the user selectsmultiple electrodes with the same ROI, ADMTP will find unique targets.For an implantation plan there are typically 7-12 electrodes, each with1000-5000 potential trajectories (for example, representingapproximately 10²¹ possible combinations. Therefore a depth-first graphstrategy may be used to calculate a feasible implementation plan. If nocombination of trajectories exists which satisfies d_(traj), ADMTPreturns the plan with the largest distance between trajectories.

It will be appreciated that the above approach for selecting atrajectory to one or more specified target locations is provided by wayof example, and other known (or future developed) approach could beused—for example, the approach set out in WO 2015/173571 (as citedabove), or as set out in certain of the References discussed above. Theapproach described herein is closely related to that set out in WO2015/173571; for convenience, the latter is described in more detailsection C below.

B) IMPLEMENTATION, EXPERIMENTAL DESIGN AND EXAMPLE RESULTS

FIG. 1 is a schematic block of the ADMTP system (tool) 100 for thepre-operative planning of electrode placement in accordance with someembodiments of the invention. The ADMTP tool 100 comprises applicationsoftware 150 running on a (modern) computer workstation such as apersonal computer (PC) 120 which is provided (inter alia) with anoperating system 130, such as Mac OS X, Linux or Windows, at least onedisplay 140, and a (modern) graphics card (graphics processing unit,GPU) 125 which functions as general-purpose parallel processor. The PC120 will generally also be provided with standard components andaccessories, such as a processor, memory (ROM/RAM), a hard disk drive,mouse, keyboard, etc, not explicitly shown in FIG. 1. The applicationsoftware 150 generally runs on one or more processors provided withinthe computer workstation 120, but numerically intensive, parallelcomputations (such as determining the metric and/or assessing differenttrajectories) are off-loaded to the GPU 125 to increase overall speed.It will be appreciated that the configuration illustrated in FIG. 1 isprovided by way of example only, and the skilled person will be aware ofmany other possible implementations on single computers (machines),distributed across two more computers, etc.

FIGS. 2A and 2B show an example of an implantation plan such as may beproduced with the apparatus of FIG. 1. In particular, FIGS. 2A and 2Bshow an implantation plan with 10 electrodes. FIG. 2A shows thefollowing deep ROIs: amygdala (cyan); hippocampus (yellow), anteriorinsula (brown), transverse temporal gyrus (blue), posterior (orange) andmiddle (purple) cingulate, posterior (green) and anterior (mauve) medialorbital gyrus. The electrodes are colour coded in accordance with theROI into which they are implanted. Note that there is one electrodeimplanted into each ROI, except there are two electrodes implanted intoeach of the hippocampus and the anterior insula. FIG. 2B showssuperficial ROIs, namely the middle frontal (blue), superior frontal(purple), middle temporal (light pink), and superior temporal (darkpink) lobes and supra-marginal (light orange), angular (light green) andprecentral (dark green) gyri. The electrodes shown in FIG. 2B are thesame as shown in FIG. 2B.

FIGS. 3A, 3B and 3C show an example of axial (FIG. 3A), sagittal (FIG.3B) and coronal (FIG. 3C) views of C_(t) (the target risk image) for thehippocampus (outlined in dark blue). In particular, the hippocampus iscoloured in the form of a heat map of the metric f_(t)(c), where redindicates a high value of the metric (low suitability), and greenindicates a low value of the metric (high suitability). FIGS. 3A, 3B and3C also show blood vessels (cyan) to be avoided, and varioustrajectories determined by ADMTP according to the approach describedherein.

In one particular experiment, the ADMTP tool has been evaluated usingretrospective data from 20 patients with medically refractory epilepsywho underwent intracerebral implantation. Patients had between 7 to 12electrodes per plan for a total of 186 electrodes. Manuals plans (MP)were determined by the consensus of two neurosurgeons using manualinspection. Deep and superficial ROIs were identified by the same twoneurosurgeons. The parameters used by the ADMTP tool for this evaluationare as set out in Table 1 (the values of Table 1 are provided by way ofexample only, and it will be appreciated that other implementations mayadopt different values).

TABLE 1 examples of parameters used for the approach described herein.The first four values in the table were determined empirically, theremainder based on the consensus of 3 neurosurgeons. Parameter Value M(number of candidate targets) 10 d_(tar) (minimum distance between 3 mmcandidate targets) ω_(roi) (relative importance 0.25 for depth in regionof interest) ω_(cri) (relative important for 0.75 distance from criticalobject) d_(len) (length threshold above 80 mm which trajectories areexcluded from consideration) d_(ang) (the most oblique entry 25 degreesangle permitted) d_(safe) (the minimum safe 3 mm distance from a bloodvessel) d_(risk) (the distance at which 10 mm there is no risk) Q (thenumber of contacts 10 per electrode) p_(q) (the interval between 6 mmcontacts) p_(r) (the contact sample 1.2 mm radius) d_(traj) 10 mm

As illustrated in FIGS. 4A-4D, electrode trajectories were assessed forsuitability on 4 quantitative measures: (a) angle with respect to theskull normal, (b) risk score, (c) distance to the nearest criticalstructure, and (d) grey matter ratio. Red circles are used to indicatethe centre of mass for each measure. For each plot of FIGS. 4A-4D, thetrajectory values are depicted such that each point corresponds to asingle trajectory with the MP value plotted on the X axis and the ADMTPvalue plotted on the Y axis. For FIGS. 4A and 4B, points below thediagonal correspond to ADMTP giving the preferred result; for FIGS. 4Cand 4D, points above the diagonal correspond to ADMTP giving thepreferred result;

A two-tailed Student's t-test was used to test the statisticalsignificance between trajectory values determined by ADMTP and MP withthe null hypothesis being that the two methods return similar values.ADMTP was found to reduce the angle in 96/186 trajectories (p=0.070),increased the grey matter ratio in 104/186 trajectories (p=0.012),reduced the disk score in 145/186 trajectories (p=4.8×10⁻¹⁴), andincreased the distance to the closest critical structure in 145/186trajectories (p=1.1×10⁻¹⁶). In general terms, ADMTP was therefore ableto find trajectories that are safer, in terms of larger distances toblood vessels, compared to MP.

The plans were further assessed based on (1) distance betweentrajectories (to avoid interference), and (2) the ratio of gyri sampledto total electrodes (which can be considered as a measure ofefficiency)—a ratio of 1 corresponds to each electrode targeting aunique gyri. In the above evaluation, ADMP was found to provide anaverage distance between trajectories of 38.3 mm (5.2-124.2 mm) comparedto MP with an average of 36.37 mm (1.28-117.47 mm). MP and ADMTP bothhave 12 electrode pairs under the d_(traj) of 10 mm (see Table 1). MPprovided an average gyri to electrode ratio of 0.96 (0.86-1) while ADMTPprovided a ratio of 0.92 (0.75-1). Thus although ADMTP has largerdistances between trajectories, MP provides better coverage in terms ofmore unique gyri sampled and more consistent coverage of the cortex.

The computational efficiency of ADMTP was assessed for computingcandidate target points, trajectories, and ADMTP (the combination ofboth steps). Computations were performed on a computer, generallycorresponding to the system 120 shown in FIG. 1, which included an IntelXeon 12 core CPU and speed of 2.10 GHz with 64 GB of RAM and a singleNVIDIA Quadro K4000 4 GB GPU. The computation times using this platformare shown in Table 2 below—ADMTP took 61.14 seconds (15.43-279.20seconds) with most of the computation time spent on trajectory planning.(To put these timings into perspective, manual planning of multipletrajectories by visual inspection typically takes 2-3 hours).

TABLE 2 computation timings, in seconds, for candidate target pointselection, trajectory planning, and ADMTP Median Time (range) Algorithmin seconds Target point selection 6.91 (3.5-19.18) Trajectory planning54.07 (7.86-264.01) ADMTP 61.14 (15.43-279.20)

FIGS. 5A and 5B shows some results from the above experiment, inparticular with the manual plan shown in pink, and the ADMTP plan shownin blue (the skull is shown in opaque white, and the cortex in peach).FIG. 5B shows target point differences within the cortex, while FIG. 5Ashows entry point differences. ADMTP often found trajectories within thesame gryus as manual planning.

C) TRAJECTORY SEARCHING

As mentioned above, WO 2015/173571 discloses an approach for searchingfor entry points and performing a trajectory risk analysis. Thisapproach may be used for selecting a trajectory to a set of one or moretarget locations such as found using the methodology described insection A above. The approach of WO 2015/173571 will now be described toprovide additional information as regards (i) entry point selection,(ii) determining the risk factor and other suitability measuresassociated with a given proposed trajectory, and (iii) accommodating thepresence of multiple electrodes so that trajectories for respectiveelectrodes do not interfere with one another. The techniques set outbelow are broadly similar to those already described in section A above,but contain additional details and minor variations. Accordingly, thefollowing description provides additional information and alternativesfor implementing the techniques already described in section A above foridentifying, assessing and selecting the trajectories to the potentialtarget locations (and ultimately to the deep ROIs).

After the potential target locations have been selected (see section Aabove), the entry points can be selected manually by the user and/orautomatically by the system 100. An entry point can only be placed ontothe skull surface (a 3D representation of the skull surface is derivedfrom the input images). The system 100 is able to analyse automaticallythe topology of the critical structures and to offer a set of potentialentry points that represent minimal risk. This entry point search is afully automated method that is implemented on the GPU 125. The methodtakes the mesh representation of the skull surface as the input andprocesses each vertex of the index, so that the sampling space and ratewhen searching for possible entry points are defined by the number andlocation of vertices in the skull model. The risk associated with eachpotential trajectory (from a potential entry point to the target point)is evaluated in real-time using the GPU 125, and unsuitable (e.g. highrisk) candidates are ruled out.

An entry point search is performed to find the set of potential entrypoints that represent minimal risk. The first portion of this searchinvolves a proximity search to find all points on the skull surfacewhich are within a predetermined distance of the target point, where thepredetermined distance reflects a maximum length for a trajectory. Thismaximum length may in turn be specified based on the physicalcharacteristics and/or operational requirements of the electrodes thatare being inserted.

The entry point candidates from the proximity search are then analysedto find the angle of entry by computing the deviation between thedirection vector of the trajectory and the surface normal of the skullat the point of entry. From a practical (surgical) point of view theentry angle should be as close to perpendicular as possible, otherwiseit becomes increasingly difficult to drill the keyhole through theskull. Therefore, if the entry angle of an entry point is outside auser-configured range (e.g. more than 10 or perhaps 20 degrees fromperpendicular), the entry point is ruled out.

Since the analysis of trajectory length and entry angle iscomputationally inexpensive, it is feasible to perform such analysis forall possible entry points (i.e. for all nodes or vertices of the surfacemesh) and then to disqualify (filter out) those entry points for whichthe trajectory is too long (greater than some threshold) or the entrypoint angle is too great (further from the perpendicular than somethreshold angle, such as 10 degrees). Each of the remaining entry pointcandidates, i.e. which are suitably close to the target point and havean acceptable entry angle, is then checked for any collision with thecritical structures. If the position of an entry point does not allowstraight access to the target point without collision with any criticalstructure, it is disqualified from the further analysis. This evaluationprocedure further reduces the number of candidate entry points.

The above filtering of entry point candidates significantly reduces thenumber of remaining entry point candidates (compared with the originalnumber of entry point candidates), thereby permitting a more detailed,automated, risk analysis of the remaining entry point candidates whilestill maintaining real-time and interactive performance. Note thatpreviously published risk metrics have generally assigned risk based onthe shortest Euclidean distance to a critical object from any point onthe trajectory. In contrast, the metric used for the present approach isbased on a cumulative risk profile which may be obtained along the fulllength of the trajectory.

The automated risk analysis starts by investigating the distance fromeach trajectory to the critical structures. For each potentialtrajectory from a candidate entry point to the target point (targetlocation), a predetermined number of sample locations are consideredalong the length of the trajectory. (In the current implementation, thepredetermined number of sample locations is 256, but otherimplementations may use a smaller or larger number, and/or may make thisnumber configurable by the user).

The system 100 now calculates, for each sample point, the minimumdistance to the critical structures (one at a time). The minimumdistance to the nearest critical object for that sample point, which isreferred to as the critical distance, is then recorded in an array whichlists, for each sample point along the trajectory, the correspondingcritical distance.

Once the critical distances have been identified for each potentialtrajectory, an overall risk computation is performed using a newintegrative risk metric that quantifies the level of risk on a 0-1 scale(0—no risk; 1—the highest risk which must be avoided). This metric canbe extended, if so desired, to include one or more additional factors asdiscussed above, such as distance and angle of entry, as well as whetherthe electrode contacts are in grey matter.

A trajectory is considered too risky if the minimum distance to anycritical structure, for any sample point on that trajectory, is lessthan a preset distance: d_(min), which can be considered as defining asafety margin. Conversely, if all sample points on a trajectory liefurther from a critical structure than a distance d_(max), which can beconsidered as defining the boundaries of a risk zone, then the criticalstructure represents no potential harm to the trajectory. Note that thevalues of d_(min) and d_(max) can be configured by the user as requiredin the system.

We can therefore quantify the overall level of risk arising fromproximity to critical structures or objects according to the following:

$\begin{matrix}{R_{dist} = {{1/L}{\int_{entry}^{target}{{f\left( {{d(x)} - d_{\min}} \right)}\ {dx}}}}} & \left( {{Eq}\mspace{14mu} 5} \right)\end{matrix}$

where x represents the distance along the trajectory to a given samplepoint, L represents the total distance from entry to target (i.e. theintegral of dx from entry to target) d(x) represents the criticaldistance for the sample point at distance x, and f(y) is a functionwhich decreases monotonically from f (y)=1 at y=0 to f (y)=0 aty≥d_(max)−d_(min). Note that:a) f(y) may be specified to have a simple straight line shape, or mayhave some other form if this better represents the variation in riskwith critical distance;b) for y<0, f (y) can be considered as undefined, or infinite. Thisvalue for y (which represents d(x)<d_(min)), would imply that thecritical distance at this sample point is less than the safety margin,d_(min), and hence this trajectory should be eliminated;c) Equation 5 yields a value for R_(dist) which is in the range of 0-1;andd) Equation 5 is presented as an integral, which in effect assumes acontinuous (infinite) distribution of sample points along thetrajectory, but it can be readily converted into a summation for afinite number of sample points.

The (predicted) risk associated with (emanating from) the entry anglecan be similarly expressed as a risk, R_(angle), based on the range ofaccepted values, likewise the risk associated with (emanating from) thelength of the trajectory: R_(length). For example, an offset angle fromthe perpendicular of 0 degrees may be assigned a risk of 0, while anoffset of (e.g.) 10 degrees may be assigned a risk of 1 (entry pointswith a higher offset angle would have already been eliminated);intermediate offset angles are then assigned an appropriate intermediaterisk value. An analogous approach can be used to assign a risk to thetrajectory length.

These independent risk components can be combined by applyingappropriate weighting factors (w) to give a total or overall risk:

R _(total) =w ₁ R _(dist) +w ₂ R _(angle) +w ₃ R _(length)  (Eq 6)

-   -   where Σw_(i)=1 and R_(i)∈[0˜1]        This final metric R_(total), which has a value between 0 (lowest        risk) and 1 (highest risk) describes the overall quality of        (risk associated with) a given trajectory. (The risk can also be        regarded as a form of cost function, where a low risk or cost is        desirable).

For the system 100, the risk evaluation is performed on the GPU 125. Toassist the proximity search and distance evaluations in completing inreal-time, a Bounding Volume Hierarchy (BVH) is built over the trianglevertices or cells of critical structures. A BVH is an acceleration datastructure that allows the fast traversal of large datasets containing 3Dpoints.

In some implementations, the system 100 further includes a risk analysismodule which is used to perform real time analysis of criticaldistances, entry angle (as above) and grey matter/white matter ratio.This latter parameter is significant, since it is generally desired toplace the electrodes used for SSEG in grey matter (for clinical reasonsto best detect the desired signals). In addition, a typical electrodemay in practice have multiple contacts which are spaced along the lengthof the trajectory. Having a high grey matter/white matter ratio improvesthe likelihood that these electrode contacts are situated in grey matter(as desired) rather than in white matter. If this ratio is below aconfigurable threshold, the user may be warned to re-consider theplacement.

It will be appreciated that there are other ways of determining a riskor cost associated with grey matter/white matter. For example, if theparticular number and location of the contacts on a given electrode areknown (e.g. based on manufacturer specifications), then it can bedetermined how many (or what fraction) of the contacts are located ingrey matter for a given trajectory, and then set the cost or riskaccordingly.

As described above, the risk analysis is performed automatically todetermine a final metric R_(total) that describes the overall quality ofany given trajectory. More particularly, this metric can be determinedfor a large number of potential trajectories. Safe zones and no-go areascan be derived as appropriate.

After the associated risks have been computed and the quality of all thepotential trajectories has been assessed, the risk values may bevisualized. The system 100 is provided with a display 140 which is usedto present a visualization of risk. For example, the system 100 supportsa colour coded display of the associated critical distance to eachsample point along the trajectory.

In many practical applications, it is generally desired to implantmultiple electrodes. These electrodes should not interfere with oneanother. Accordingly, the placement of each entry point should also takeinto consideration the placement of the other entry points (for othertarget locations). One way of achieving this is to determine an entrypoint using the above approach for each target point in turn.Trajectories that have already been determined for previous targetpoints can then be treated as critical structures to be avoided (as forthe other critical structures), as each new trajectory is determined.Although this approach is feasible, it is dependent on the ordering inwhich the target points are chosen for determining a trajectory, and maytherefore not provide the best overall solution. The system 100therefore incorporates a multiple trajectory planning process whichavoids such sensitivity on target point ordering. The process startswith the user selecting the desired set of N target points T_(i): i∈{1,. . . , N}. For each target point in this set, the entry point searchand risk analysis procedure is run independently to obtain a set of Mpotential entry points E_(i,j): j∈{1, . . . , M}—i.e. entry points thatare not excluded or filtered out by virtue of being too long, having anunacceptable entry angle, or going directly through a critical structureto get to the target point. Each potential target/entry point pair in aset M is represented by a trajectory T_(i)E_(i,j) . The risk analysisprocedure described above then returns a risk score R_(i,j) thatdescribes the overall quality for each T_(i)E_(i,j) trajectory in M(note R_(i,j) corresponds to R_(total) in Equation (6) above).

The multiple trajectory planning procedure seeks to find an optimalcombination of trajectories for all of the N target points such thatthey do not interfere with one other. Trajectory interference isconsidered to occur if the minimum distance between any two trajectoriesis smaller than a “Trajectory Margin” distance: d_(min). This trajectorymargin distance can be configured by the user, and may be set asappropriate to be the same as, or different from, the d_(min) used tospecify the safety margin around the critical structures.

The optimal combination of N trajectories can then be calculated as acombination of the risk scores for each trajectory:

$\begin{matrix}{R_{all} = {\min\limits_{R_{all}}{\frac{1}{N}{\sum\limits_{i = 1}^{N}{R_{i,j}:{j \in \left\{ {1,\ldots \mspace{14mu},\; M} \right\}}}}}}} & {{Equation}\mspace{14mu} (7)}\end{matrix}$

subject to D(T_(i)E_(i,j) , T_(k)E_(k,j) >d_(min): ∀i∈{1, . . . , N},∀k∈{1, . . . , N}, i≠k. Note that D(T_(i)E_(i,j) , T_(k)E_(k,j) )represents the minimum distance between the two trajectoriesT_(i)E_(i,j) and T_(k)E_(k,j) . The final metric R_(all) describes theoverall quality (risk or cost) of the trajectory combination on a 0-1scale (0—no risk; 1—the highest risk which must be avoided).

To support real-time optimization of R_(all), a dynamic programmingdepth-first search algorithm is utilized. The algorithm starts with aninitial value of n=1, and proceeds as follows:

1) Find the combination of n trajectories having the (next) lowest riskscore.2) Evaluate if D(T_(i)E_(i,j) , T_(k)E_(k,j) )>d_(min) for all ntrajectories.3) If false return to (1); if true find the next lowest risk score forn+1 trajectories (and increment n).4) Return when a valid configuration for n=N is found.

In this approach, the target points or trajectories are effectivelyadded in one at a time. For n=1, step 1 is simple, it just involvesselecting the trajectory for that target location which has the lowestoverall risk factor (on an individual basis). For n=1, step 2necessarily returns true (because a single trajectory cannot have aconflict with itself). We therefore return to step 1, and include thetrajectory for a second target point, in particular the trajectory whichhas the lowest overall risk factor (on an individual basis) for thissecond target point. However, if a conflict is found at step 2, i.e. thedistance between the new trajectory and the existing (already included)trajectory is less than d_(min), then selected entry points arereconsidered at step 1 for both of the target points (not just the newlyadded target point) to find the (next) lowest combination of riskfactors for the two target locations. This procedure is then repeated,adding in another target location at a time, until all N trajectorieshave been determined. Note that by considering only the lowest validcombination of trajectories at step 1 of each iteration, the output ofthis algorithm produces the optimal (in terms of risk scoreminimization) configuration of electrodes.

D) FLOWCHART

The present approach provides a method of using a computer system toassist in planning a trajectory for a surgical insertion into a skull toa target representing an anatomical region, as well as an apparatus andcomputer program for implementing such a method.

FIG. 6 shows a high-level flowchart corresponding to this method, whichbegins with providing the computer system with a three-dimensional imagerepresentation of the skull and brain which has been parcellated intoanatomical regions (610). For example, the image may comprise a set ofvoxels (pixels in 3D space) with each voxel labelled according to theanatomical region in which the voxel is located. Thus the image isformed of voxels, each voxel representing a spatial coordinate with anassociated label indicating the anatomical region at that spatialcoordinate. However, the image representation may be provided inalternative formats. For example, the image representation may beprovided as a listing of anatomical regions, with each region beingnamed, and provided with a geometrical expression or model (e.g. mesh)defining the surface of the region.

Furthermore, the computer system is provided with an identification ofcritical objects comprising structures within the skull to be avoidedduring the surgical insertion. This identification includes the locationof the critical objects within the three-dimensional representation. Thethree-dimensional image representation and critical object segmentationcan generally be obtained from CT and/or MR imaging (for example).

A user also provides the computer system with one or more regions ofinterest (620), each region of interest corresponding to an anatomicalregion within the skull representing the target of the trajectory forthe surgical insertion. For example, the user may provide the labelsassociated with the anatomical region, such that they can be found bythe system in the three-dimensional image representation.

The system now determines a metric for voxel locations within theanatomical region(s) corresponding to the region of interest(s) (630).The metric represents the suitability of each of the voxel locations tobe a target location for the trajectory for a surgical insertion. (Inthe following discussion, we assume a high value of the metricrepresents a good suitability, but it will be appreciated that in someimplementations, a good suitability might be represented by a low valueof the metric). In some cases, the metric may be calculated for eachvoxel in a region of interest, in other cases, the metric may bedetermined for only a subset of the voxels (e.g. every other voxel) toreduce processing costs. A further possibility is that the voxellocations are specified as a separate set of spatial coordinates (e.g. agrid) which can be mapped onto the region of interest in thethree-dimensional image representation.

For each region of interest, a set of one or more voxel locations havingthe greatest (or good) suitability according to the metric is selected(640). Each of the one or more selected voxel locations represents apotential target location for the trajectory. The system then identifiesa trajectory to a potential target location in the region of interestwhich can be used for the surgical insertion (step 650).

In many practical implementations, multiple potential target locationsare identified within a region of interest—for example, corresponding tolocal maxima of the metric within the region of interest. Identifying atrajectory for the surgical insertion may then comprise determining oneor more trajectories to each of the multiple potential target locations(652), and then selecting the best of these trajectories (654), forexample based on lowest risk factor (and any other relevant factors,e.g. GM sampling), to use for the surgical insertion to the region ofinterest. In some cases, multiple trajectories are determined atoperation 652 to each of the potential target locations. At operation654, the best trajectory for each potential target location is firstdetermined (according to criteria discussed above), and then the bestoverall trajectory for the region of interest is identified from thebest trajectories for each potential target location.

Accordingly, it will be appreciated that there are many differentimplementations according to circumstances. For example, in some cases,the metric might be used to determine a single target location within aregion of interest (e.g. representing a maximum value of the metricwithin the region of interest). The system then determines a trajectoryfor the surgical insertion to this single target location. In the eventthat no suitable trajectory can be found, the system may then identify asecond potential target location (e.g. based on a subsidiary orneighbouring peak of the metric), and try to find a good trajectory tothis second potential target location. In other cases, the metric mightbe used to determine multiple potential target locations, andcorresponding (best) trajectories are determined for each potentialtarget location. This latter approach may give a more suitabletrajectory for the surgical insertion, in that the best trajectoryoverall to a given region of interest may not necessarily terminate atthe most suitable target location. In addition, in some cases, e.g. fora large region of interest, two (or more) electrodes might be insertedinto the same region of interest, and these can use different potentialtarget locations for the different electrodes.

The metric is generally independent of any trajectory leading to thevoxel location for which the metric is being calculated. For example,the metric may be based on distance from the nearest critical objects,such that the suitability of a voxel location increases with distancefrom the nearest surface of the critical objects, and/or on distanceinside the region of interest, such that the suitability of a voxellocation increases with distance inside the region of interest. Thisallows the metric to be calculated relatively quickly (e.g. becausethere is no set of calculations to be repeated at each location alongthe path of a trajectory).

A potential target location can be identified as a maximum (or localmaximum) of the metric using any suitable approach, e.g. a hill-climbingalgorithm, subject to the accuracy of the maximum finding technique. Forexample, in some cases the analysis may not locate the precise maximum,but rather identify a location having a relatively high value—e.g. inthe vicinity or neighbouring to the precise maximum. For presentpurposes, a relatively high value of the metric (compared to surroundingor all other voxels) will be considered as representing locations havingthe greatest suitability according to the metric.

If multiple potential target locations are being located within a givenregion of interest, the assessment of suitability may take intoconsideration the distance between the potential target locations—ifthey are very close together, this in effect reduces the spread oftrajectories to the region of interest that will subsequently beconsidered. Accordingly, the system may, for example, impose aconstraint on selecting the set of one or more voxel locations torepresent potential target locations, the constraint requiring apredetermined minimum distance between the potential target locationswithin the region of interest. This can improve robustness in finding agood trajectory to the region of interest by considering a wider spreadof trajectories. The number of potential target locations to selectwithin a given region of interest depends upon the particularcircumstances (including clinical need), but may for example fall in therange 5 to 25, e.g. preferably 8 to 15.

In many implementations, a user will select multiple regions ofinterest, and a respective trajectory will then be identified for eachregion of interest. This identification may be performed in a similarmanner to the approach set out above for a single region of interest,usually with the added constraint that the trajectories for differentelectrodes (e.g. to different regions of interest) must not conflict.

E) CONCLUSION

Compared to manual planning, the use of ADMTP as described here has beenfound to lower risk for 76% of trajectories and to increase GM samplingin 56% of trajectories (according to the experimental results obtainedabove). In addition, ADMTP is relatively inexpensive in computationalterms, such that trajectories can be determined in real-time (or atleast near real-time). This suggests that ADMTP is a feasible andpotentially valuable tool for use in computer-assisted planning of oneor more trajectories for surgical insertion into a skull.

Various embodiments of the invention have been described above. Theskilled person will appreciated that the features of these embodimentsmay be combined with one another as appropriate, or modified accordingto the particular circumstances of any given application. The scope ofthe invention is defined by the appended claims and their equivalents.

REFERENCES

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1. A method of using a computer system to assist in planning atrajectory for a surgical insertion into a skull to a targetrepresenting an anatomical region, the method comprising: providing thecomputer system with a three-dimensional image representation of theskull and brain which has been parcellated into anatomical regions,including an identification of critical objects comprising structureswithin the brain to be avoided during the surgical insertion; providingthe computer system with a region of interest comprising an anatomicalregion within the brain representing the target of the trajectory forthe surgical insertion; determining a metric for voxel locations withinthe anatomical region corresponding to the region of interest, themetric representing the suitability of each of the voxel locations to bea target location for the trajectory; selecting a set of one or morevoxel locations having the greatest suitability according to the metricwithin the region of interest, each of the one or more selected voxellocations representing a potential target location for the trajectory;and identifying a trajectory for the surgical insertion to a potentialtarget location in the region of interest.
 2. The method of claim 1,wherein the set of one or more voxel locations comprises multiple voxellocations, each corresponding to a respective potential target location,and wherein identifying a trajectory comprises: for each of thepotential target locations, determining one or more trajectories to thatpotential target location; and selecting the trajectory for the surgicalinsertion from the determined trajectories to the multiple potentialtarget locations.
 3. The method of claim 2, further comprising: for eachof the potential target locations, determining multiple trajectories tothat potential target location; for each of the potential targetlocations, identifying an optimal trajectory to that potential targetlocation from said multiple trajectories; and selecting the trajectoryfor the surgical insertion from the optimal trajectories to the multiplepotential target locations.
 4. The method of claim 1, wherein theselection of the trajectory for the surgical insertion from thedetermined or optimal trajectories is based on at least trying tominimise a risk factor associated with the trajectories.
 5. The methodof claim 1, wherein the metric is independent of any trajectory leadingto the voxel location for which the metric is being calculated.
 6. Themethod of claim 1, wherein the metric is based on at least one of:distance from the nearest critical objects, such that the suitability ofa voxel location increases with distance from the nearest surface of thecritical objects; distance inside the region of interest, such that thesuitability of a voxel location increases with distance inside theregion of interest; proximity to a medial surface of the region ofinterest, such that the suitability of a voxel location within theregion of interest increases with closeness to the medial surface; and aweighted sum of a first factor representing distance inside the regionof interest and a second factor based on distance from the nearestsurface of the critical objects.
 7. (canceled)
 8. (canceled) 9.(canceled)
 10. The method of claim 1, wherein the set of one or morevoxel locations comprises multiple voxel locations, each voxel locationin the set corresponding to a respective potential target location, andeach voxel location in the set representing a local maximum of themetric.
 11. The method of claim 10, further comprising imposing aconstraint on selecting the set of one or more voxel locations torepresent potential target locations, said constraint requiring apredetermined minimum distance between the potential target locationswithin the region of interest.
 12. The method of claim 1, wherein saidselecting a set of one or more voxel locations includes: generating aset of multiple candidate voxel locations; and spatially clustering themultiple candidate voxel locations.
 13. The method of claim 12, whereinN electrodes (N>1) are to be inserted into said region of interest, andsaid spatial clustering is configured to form N clusters, each clustercorresponding to a respective electrode for insertion.
 14. The method ofclaim 12, wherein selecting a set of one or more voxel locationscomprises selecting one or more voxel locations for each cluster fromsaid spatial clustering.
 15. The method of claim 14, wherein, for eachcluster, the one or more voxel locations are selected that have thelargest suitability according to the metric.
 16. The method of claim 12,wherein the set of multiple candidate voxel locations is generated byiterative flooding.
 17. The method of claim 1, further comprisingproviding multiple regions of interest, and selecting a respectivetrajectory for each region of interest.
 18. The method of claim 17,wherein the trajectories for the regions of interest are subject to aconstraint to reduce potential interference between trajectories toregions of interest.
 19. The method of claim 1, wherein the set of oneor more voxel locations comprises M voxel locations, each correspondingto a respective potential target location, where M is in the range 3 to50, preferably 5 to 25, preferably 8 to
 15. 20. The method of claim 1,wherein the three-dimensional image representation comprises data fromcomputed tomography and/or magnetic resonance imaging.
 21. The method ofclaim 1, wherein the surgical insertion is to be used for implanting anelectrode into the skull.
 22. A computer-readable medium comprisingprogram comprising program instructions in machine-readable format thatwhen executed by one or more processors in a computer system cause thecomputer system to implement the method of any preceding claim. 23.(canceled)
 24. A computer system to assist in planning a trajectory fora surgical insertion into a skull to a target representing an anatomicalregion, the computer system being configured to: provide the computersystem with a three-dimensional image representation of the skull whichhas been parcellated into anatomical regions, including anidentification of critical objects comprising structures within theskull to be avoided during the surgical insertion; provide the computersystem with a region of interest comprising an anatomical region withinthe skull representing the target of the trajectory for the surgicalinsertion; determine a metric for voxel locations within the anatomicalregion corresponding to the region of interest, the metric representingthe suitability of each of the voxel locations to be a target locationfor the trajectory; select a set of one or more voxel locations havingthe greatest suitability according to the metric within the region ofinterest, each of the one or more selected voxel locations representinga potential target location for the trajectory; and identify atrajectory for the surgical insertion to a potential target location inthe region of interest.
 25. (canceled)
 26. (canceled)